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Estimating $PM_{2.5}$ Concentrations Using an Improved Land Use Regression Model in Zhejiang, China
| Content Provider | MDPI |
|---|---|
| Author | Zheng, Sheng Zhang, Chengjie Wu, Xue |
| Copyright Year | 2022 |
| Description | Fine particulate matter $(PM_{2.5}$) pollution affects the environment and poses threat to human health. The study of the influence of land use and other factors on $PM_{2.5}$ is crucial for the rational development and utilization of territorial space. To explore the intrinsic mechanism between $PM_{2.5}$ pollution and related factors, this study used the land use regression (LUR) model, and introduced geographically weighted regression (GWR), and random forest (RF) to optimize the basic LUR model. The basic LUR model was constructed to predict the annual average $PM_{2.5}$ concentrations using three elements: artificial surfaces, forest land, and wind speed as explanatory variables, with adjusted $R^{2}$ of 0.645. The improved LUR models based on GWR and RF, with an adjusted $R^{2}$ of 0.767 and 0.821, respectively, show better fitting effects. The LUR simulation results show that the $PM_{2.5}$ pollution in the northern Zhejiang is more serious and concentrated. The concentrations are also higher in regions such as the river valley plains in central Zhejiang and the coastal plains in southeastern Zhejiang. These findings show that pollution emissions should be further reduced and environmental protection should be strengthened. |
| Starting Page | 1273 |
| e-ISSN | 20734433 |
| DOI | 10.3390/atmos13081273 |
| Journal | Atmosphere |
| Issue Number | 8 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-11 |
| Access Restriction | Open |
| Subject Keyword | Atmosphere Environmental Engineering Pm2.5 Land Use Regression Model Geographically Weighted Regression Random Forest Zhejiang Province |
| Content Type | Text |
| Resource Type | Article |
